Paper Info
Title
The Impact of the Number of Eigen-Faces on the Face Recognition Accuracy using Different Distance Measures
Abstract
The embedded and real-time systems are the main motivation for this research where the computations are critical to be reduced as much as possible. Face recognition method using eigen-faces yields good accuracy if enough eigen-faces are considered in the classification process. The more eigen-faces used, the more computation power is needed. In this paper, the main goal is to investigate the trade-off between the used number of eigen-faces and the accuracy and the needed computation power of face recognition. Three different distance measures are studied. Namely: Euclidean, block-city, and chess board distances are used. It is concluded that there is some optimum number of eigen-faces that provides the highest recognition rate and acceptable execution time. Moreover, the best number of eigen-faces highly depends on the selected distance measure.
Year
DOI
Venue
2018
10.1109/AICCSA.2018.8612837
2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA)
Keywords
Field
DocType
Eigen-Faces,Face Recognition,Distance Measures,PCA
Facial recognition system,Pattern recognition,Computer science,Feature extraction,Real-time computing,Artificial intelligence,Execution time,Euclidean geometry,Principal component analysis,Computation,Distance measures
Conference
ISSN
ISBN
Citations 
2161-5322
978-1-5386-9121-2
0
PageRank 
References 
Authors
0.34
3
4
Name
Order
Citations
PageRank
Yousef Shatnawi100.34
Mohammad A. Alsmirat213016.98
Mahmoud Al-Ayyoub373063.41
Monther Aldwairi47611.84